Gaming desire: Online behavioural tracking in Facebook

In a previous blog I examined how researchers in the gambling studies field are using online behavioural tracking data to study excessive gambling. However, online tracking data is used all the time in lots of contexts by lots of companies to better understand human behaviour. Most people view the internet as a very anonymous medium but rest assured, someone, somewhere, is monitoring everything you do online. The internet is the most non-anonymous place I can possibly think of.

Data from online environments provide potential advantages to many sectors (particularly those in the fields of marketing and advertising). This is because every click and transaction of every customer is automatically stored in a database and provides very high data quality for those who know what to do with it. This is very much in contrast to market surveys where such data have to be gathered at high costs, with lots of manual work involved, and may be prone to error. Within my own gambling studies field, a number of different teams across the world (including my own) have now been given access to behavioural tracking data of gamblers. Such data include:

• Account data (i.e., general information about each player) including user ID, date of birth, gender, other demographic data (if available), and geographic data (e.g., postal codes).
• Game data (for each player) including game ID, game type (if available), amount of winnings, amount of money spent, number of bets/spins (or equivalent), chasing behaviour, bonus usage, and session time.
• Financial data (for each player, and game, if applicable) including the number and amount of deposits, the number of money withdrawals, and the number of accounts.
• Responsible gaming limit data (i.e., time, spending, etc) for each player, and game (if applicable) including current time and spend limits, changes of limits, and the number of limit strikes (i.e., when players reach their pre-determined limit for deposits or time.).
• Miscellaneous data for each player (and game if applicable) including the length of playing session, login information, number of ‘cool-off periods’ (when players lock themselves out of their accounts for pre-defined periods of time), and any other data that might be relevant or of interest.

Many of these types of data are also held by those who operate games via social networking sites and could potentially be used for research purposes. Online social networking sites (e.g., Facebook) typically collect two types of information. These are (i) personal details provided by a user (e.g., name, email address, telephone number, address, gender and schools attended, etc.) and (ii) usage data collected automatically as the user clicks from one webpage to another (e.g., how users access the site, type of web browser used, time spent logged onto the site, etc.). Other day-to-day types of data collected from social network users include:

• The IP address used every time a user logs on to the social networking site.
• Every friend request a user has ever received and how they responded.
• A list of all users’ family and friends (including relationship status)
• Every pressing of ‘Like’ that a user has used on a wide variety of topics and events.
• Credit card information, if the user ever purchased credits or advertising on the social networking site.
• Every poke a user has exchanged (including both pokes made by the user on someone else and a record of all the times a user has been poked).
• Every event a user has been invited to through via the networking site and how they responded.
• Dates of user name changes and historical privacy settings changes.
• Photographs of life events and camera metadata including time stamps and latitude/longitude of picture location, as well as tags from photos – even if users have untagged them.
• The last known physical location of the user, with latitude, longitude, time/date, altitude, and more.
• A history of every private and public message and chat on the social networking site.
• Lists of every song the user has listened to online and every article that they have read.

Almost any variable (beyond the usual demographic ones such as age, gender, education, relationship status, etc.) could be examined as a risk and/or protective factor in the context of social gaming (e.g., Is the number of credit cards registered related to time and/or money spent on social gaming? How often does a person access a social game following the recommendation of another person? Does the type of social gaming differ by geographical location of where the user logged on? Does poking facilitate social gaming?). Facebook currently stores up to 800 pages of data on each of its users.

It has also been revealed that some social networking sites (e.g., Facebook) collect data from users who have logged out (usually – say the company – “for security purposes”) through the use of ‘cookies’. Cookies relay information from any site a user visits that contains a link to the social networking site such as a ‘like’ button.  With these data, the company can map out any users more general web usage.

Access to such data sets may provide insights into a number of important areas such as transitions from social gaming to social gambling, and how social gaming might be integrated with other online behaviours (such as gambling). However, other types of research are also needed including longitudinal studies so that people can be tracked over time (either via behavioural tracking and/or surveys), and more detailed qualitative work (interviews and focus groups) with different player types (e.g., regular and/or problematic gamblers/social gamers).

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Delfabbro, P.H., King, D.L & Griffiths, M.D. (2012). Behavioural profiling of problem gamblers: A critical review. International Gambling Studies, 12, 349-366.

Griffiths, M.D. (2003). Internet gambling: Issues, concerns and recommendations. CyberPsychology and Behavior, 6, 557-568.

Griffiths, M.D. (2009). Social responsibility in gambling: The implications of real-time behavioural tracking. Casino and Gaming International, 5(3), 99-104.

Griffiths, M.D. (2013). Social gambling via Facebook: Further observations and concerns. Gaming Law Review and Economics, 17, 104-106.

Griffiths, M.D. & Parke, J. (2002). The social impact of internet gambling. Social Science Computer Review, 20, 312-320.

Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

Griffiths, M.D. & Wood, R.T.A. (2008). Gambling loyalty schemes: Treading a fine line? Casino and Gaming International, 4(2), 105-108.

Kuss, D.J. & Griffiths, M.D. (2011). Online social networking and addiction: A literature review of empirical research. International Journal of Environmental and Public Health, 8, 3528-3552.